{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FLUGFIWBXRO4ATND46TT5N6GQY","short_pith_number":"pith:FLUGFIWB","schema_version":"1.0","canonical_sha256":"2ae862a2c1bc5dc04da3e7a73eb7c68629c4687174be647886961e68be22fc9a","source":{"kind":"arxiv","id":"2607.17457","version":1},"attestation_state":"computed","paper":{"title":"The Matryoshka Hypencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Majd Alkawaas, Sean MacAvaney","submitted_at":"2026-07-20T01:10:54Z","abstract_excerpt":"The Hypencoder is a recently-proposed retrieval approach that encodes queries as shallow neural networks (\"Q-Nets\") that estimate relevance over pre-computed document embeddings. Inspired by Matryoshka Representation Learning, we show that the Hypencoder can be extended to support multiple sizes of Q-Nets, allowing trade-offs between effectiveness and efficiency when deployed. We find that this \"Matryoshka Hypencoder\" achieves comparable in-domain effectiveness with approximately 7x fewer active parameters in-domain and half as many active parameters out-of-domain, which corresponds to a 1.6-3"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.17457","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-07-20T01:10:54Z","cross_cats_sorted":[],"title_canon_sha256":"04699a9a0f51be045d52bc996377671b13096d906df18016a0b04f25cb07b622","abstract_canon_sha256":"34c9273e4a893ef61455924210cdf40ec9d3e1f7c428335cb82e98fea89d6f31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:33.815720Z","signature_b64":"BqfFsvFnmMLDWSS6y4mnGyeEf655tETl0iIL1V7mYsuNMF3RwYifft4etd/SsaKlNwrS+YNuIMaKhvN+6zmaCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ae862a2c1bc5dc04da3e7a73eb7c68629c4687174be647886961e68be22fc9a","last_reissued_at":"2026-07-21T01:21:33.814854Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:33.814854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Matryoshka Hypencoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Majd Alkawaas, Sean MacAvaney","submitted_at":"2026-07-20T01:10:54Z","abstract_excerpt":"The Hypencoder is a recently-proposed retrieval approach that encodes queries as shallow neural networks (\"Q-Nets\") that estimate relevance over pre-computed document embeddings. Inspired by Matryoshka Representation Learning, we show that the Hypencoder can be extended to support multiple sizes of Q-Nets, allowing trade-offs between effectiveness and efficiency when deployed. We find that this \"Matryoshka Hypencoder\" achieves comparable in-domain effectiveness with approximately 7x fewer active parameters in-domain and half as many active parameters out-of-domain, which corresponds to a 1.6-3"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17457","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.17457/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.17457","created_at":"2026-07-21T01:21:33.815270+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17457v1","created_at":"2026-07-21T01:21:33.815270+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17457","created_at":"2026-07-21T01:21:33.815270+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLUGFIWBXRO4","created_at":"2026-07-21T01:21:33.815270+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLUGFIWBXRO4ATND","created_at":"2026-07-21T01:21:33.815270+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLUGFIWB","created_at":"2026-07-21T01:21:33.815270+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY","json":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY.json","graph_json":"https://pith.science/api/pith-number/FLUGFIWBXRO4ATND46TT5N6GQY/graph.json","events_json":"https://pith.science/api/pith-number/FLUGFIWBXRO4ATND46TT5N6GQY/events.json","paper":"https://pith.science/paper/FLUGFIWB"},"agent_actions":{"view_html":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY","download_json":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY.json","view_paper":"https://pith.science/paper/FLUGFIWB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17457&json=true","fetch_graph":"https://pith.science/api/pith-number/FLUGFIWBXRO4ATND46TT5N6GQY/graph.json","fetch_events":"https://pith.science/api/pith-number/FLUGFIWBXRO4ATND46TT5N6GQY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY/action/storage_attestation","attest_author":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY/action/author_attestation","sign_citation":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY/action/citation_signature","submit_replication":"https://pith.science/pith/FLUGFIWBXRO4ATND46TT5N6GQY/action/replication_record"}},"created_at":"2026-07-21T01:21:33.815270+00:00","updated_at":"2026-07-21T01:21:33.815270+00:00"}